Abstract
Field-Programmable Gate Arrays (FPGAs) are becoming increasingly important in embedded and high-performance computing systems. They allow performance levels close to the ones obtained with Application-Specific Integrated Circuits, while still keeping design and implementation flexibility. However, to efficiently program FPGAs, one needs the expertise of hardware developers in order to master hardware description languages (HDLs) such as VHDL or Verilog. Attempts to furnish a high-level compilation flow (e.g., from C programs) still have to address open issues before broader efficient results can be obtained. Bearing in mind an FPGA available resources, it has been developed LALP (Language for Aggressive Loop Pipelining), a novel language to program FPGA-based accelerators, and its compilation framework, including mapping capabilities. The main ideas behind LALP are to provide a higher abstraction level than HDLs, to exploit the intrinsic parallelism of hardware resources, and to allow the programmer to control execution stages whenever the compiler techniques are unable to generate efficient implementations. Those features are particularly useful to implement loop pipelining, a well regarded technique used to accelerate computations in several application domains. This paper describes LALP, and shows how it can be used to achieve high-performance computing solutions.

Abstract
Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. © 2012 Springer-Verlag.

Abstract
This paper presents a visual localization approach that is suitable for domestic and industrial environments as it enables accurate, reliable and robust pose estimation. The mobile robot is equipped with a single camera which update sits pose whenever a landmark is available on the field of view. The innovation presented by this research focuses on the artificial landmark system which has the ability to detect the presence of the robot, since both entities communicate with each other using an infrared signal protocol modulated in frequency. Besides this communication capability, each landmark has several high intensity light-emitting diodes (LEDs) that shine only for some instances according to the communication, which makes it possible for the camera shutter and the blinking of the LEDs to synchronize. This synchronization increases the system tolerance concerning changes in brightness in the ambient lights over time, independently of the landmarks location. Therefore, the environment's ceiling is populated with several landmarks and an Extended Kalman Filter is used to combine the dead-reckoning and landmark information. This increases the flexibility of the system by reducing the number of landmarks required. The experimental evaluation was conducted in a real indoor environment with an autonomous wheelchair prototype.

Abstract

Abstract
In this work a novel optical-fiber sensor for carbon dioxide measurement is presented. A polymeric sensitive layer based on the acid-base equilibrium of phenol and of its derivative 4-nitro-phenol is used for carbon dioxide determination. The sensitive material presents changes in color and in its refractive index. Colorimetric and refractometric measurements were performed. The results show the sensor is more sensitive for lower concentrations and a saturation effect occurs for higher levels. For the colorimetric response, a resolution of +/- 0.15% was estimated and a response time of 30s was measured. For the refractometric measurements, a resolution of +/- 0.50% could be estimated and a response time of 12s was measured. Reversibility and reproducibility were also demonstrated.

Abstract
An interferometric Fabry-Perot cavity based on hollow-core ring photonic crystal fiber (HCR-PCF) for pressure sensing is proposed. The sensing head is formed by splicing a small section of HCR-PCF to standard single mode fiber. The spectral response depends on the cavity length due to the geometry of the HCR-PCF. The sensing head is subjected to methane pressure variations, where it exhibits a sensitivity of 0.82 nm/MPa. Its response to nitrogen pressure variation is also studied. The sensing head's intrinsic sensitivity to the nitrogen refractive index variations inside the hollow-core is also estimated. Finally, temperature measurement is performed and a sensitivity of 3.77 pm/degrees C is obtained for temperatures below 200 degrees C.